
import warnings
warnings.filterwarnings('ignore')


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


df0 = pd.read_csv("hotel_bookings.csv")
print(df0.shape)
df0.head()

(119390, 32)


df0.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


df0.isna().sum()

hotel                                  0
is_canceled                            0
lead_time                              0
arrival_date_year                      0
arrival_date_month                     0
arrival_date_week_number               0
arrival_date_day_of_month              0
stays_in_weekend_nights                0
stays_in_week_nights                   0
adults                                 0
children                               4
babies                                 0
meal                                   0
country                              488
market_segment                         0
distribution_channel                   0
is_repeated_guest                      0
previous_cancellations                 0
previous_bookings_not_canceled         0
reserved_room_type                     0
assigned_room_type                     0
booking_changes                        0
deposit_type                           0
agent                              16340
company                           112593
days_in_waiting_list                   0
customer_type                          0
adr                                    0
required_car_parking_spaces            0
total_of_special_requests              0
reservation_status                     0
reservation_status_date                0
dtype: int64


# taking care of missing values:
df0.fillna(0,inplace = True)


# converting the reservation_status_date to date/time format:
df0["reservation_status_date"] = pd.to_datetime(df0["reservation_status_date"])


#finding the number of unique values:
temp_list = ['adults', 'children','babies']
for i in temp_list:
    print(f"{i} has unique values as: \t {df0[i].unique()}")

adults has unique values as: 	 [ 2  1  3  4 40 26 50 27 55  0 20  6  5 10]
children has unique values as: 	 [ 0.  1.  2. 10.  3.]
babies has unique values as: 	 [ 0  1  2 10  9]


filter = (df0["children"] == 0) & (df0["adults"] == 0) & (df0["babies"] == 0)
df0[filter].shape

(180, 32)


df = df0[~filter] #excluding the filtered data by using "~" sign


#spatial analysis:
#where did the guests come from?
country_wise_data = df[df['is_canceled'] == 0]['country'].value_counts().reset_index()
country_wise_data.columns = ["country","no. of guests"]
country_wise_data


# !pip install folium
# !pip install plotly


import folium
from folium.plugins import HeatMap
import plotly.express as px


basemap = folium.Map()


map_guest = px.choropleth(country_wise_data, 
              locations = country_wise_data["country"],
              color = country_wise_data["no. of guests"],
              hover_name = country_wise_data["country"],
              title = "Home country of guests")
map_guest.show()


## we set a new dataset where "is_canceled" is 0, in order to assess the features:
data_0 = df[df["is_canceled"]==0]


# answeing the second question: How much do guests pay for a night?
plt.figure(figsize = (12,8))
sns.boxplot(x="reserved_room_type", y = "adr", hue = "hotel", data = data_0)
plt.title("Price of room types per night & per person")
plt.xlabel = ("Room Type")
plt.ylabel = ("Price(Euro)")
plt.legend()
plt.show();


# We are going to check the prices per night separately for Resort and City hotels;
resort_hotel_data = df[(df["hotel"] == "Resort Hotel") & (df["is_canceled"]== 0)]
city_hotel_data = df[(df["hotel"] == "City Hotel") & (df["is_canceled"]== 0)]


resort_hotel_data.head()


city_hotel_data.head()


# grouping each of the generated dataframes by "month":
resort_hotel_price = resort_hotel_data.groupby(["arrival_date_month"])["adr"].mean().reset_index()
city_hotel_price = city_hotel_data.groupby(["arrival_date_month"])["adr"].mean().reset_index()
# merging the two dataframes:
final_data = resort_hotel_price.merge(city_hotel_price, on= "arrival_date_month")
final_data.columns = ["month","price_for_resort","price_for_city_hotel"]


resort_hotel_price


city_hotel_price


final_data.head(12)
# notice that the table is not sorted (by month):


# #sorting the "final_data" by month using additional packages:
# !pip install sorted-months-weekdays
# !pip install sort-dataframeby-monthorweek
# ;


import sort_dataframeby_monthorweek as sd


# sorting the data by month:
final_data = sd.Sort_Dataframeby_Month(final_data, "month")
final_data.head(12)


#
px.line(final_data, 
        x = "month", 
        y = ["price_for_resort", "price_for_city_hotel"], 
        title = "Room price per night over the months")


# Which months are the busiest?
# we use "arrival_date_month" column and count the values:
resort_hotel_arrival = resort_hotel_data["arrival_date_month"].value_counts().reset_index()
resort_hotel_arrival.columns = ["month", "no. of guests"]  #changing the columns names for better demonstration

city_hotel_arrival = city_hotel_data["arrival_date_month"].value_counts().reset_index()
city_hotel_arrival.columns = ["month", "no. of guests"] #changing the columns names for better demonstration


resort_hotel_arrival


city_hotel_arrival


# merging the two dataframes
final_arrival = resort_hotel_arrival.merge(city_hotel_arrival,on = "month")
final_arrival.columns = ["month", 'no. of guests in resort','no. of guests in city hotel']
final_arrival = sd.Sort_Dataframeby_Month(final_arrival,"month")
final_arrival


px.line(data_frame = final_arrival, 
        x = "month", 
        y = ["no. of guests in resort", "no. of guests in city hotel"], 
        title='Total no of guests per Months')


#How long do people stay at the hotels?
# In order to answer to this question we combine the total nights of week days and weekends and we create a new column called 'total nights'
# we use data_0 which is the cleaned dataset where guests have not canceled their reservations; (is_canceled == 0)
data_0["total_nights"] = data_0 ["stays_in_week_nights"] + data_0 ["stays_in_weekend_nights"]
data_0.head()


data_0.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date', 'total_nights'],
      dtype='object')


stay_data = data_0.groupby(["total_nights", "hotel"]).agg("count").reset_index()
stay_data = stay_data.iloc[:,0:3] #only keeping related columns
# we need to rename the "is_canceled" to "total numbers of stay"
stay_data = stay_data.rename(columns={"is_canceled":"total_numbers_of_stay"})
stay_data.head()


plt.figure(figsize=(21,10))

sns.barplot(x = 'total_nights', y = 'total_numbers_of_stay', hue = 'hotel', 
            hue_order = ['City Hotel', 'Resort Hotel'], data = stay_data)
plt.legend(loc ='right');


df.corr()


# we only need the "is_canceled" corelation with other attributes;
corelation = df.corr()['is_canceled'].abs().sort_values(ascending = False)[1:]
corelation

lead_time                         0.292876
total_of_special_requests         0.234877
required_car_parking_spaces       0.195701
booking_changes                   0.144832
previous_cancellations            0.110139
is_repeated_guest                 0.083745
company                           0.083594
adults                            0.058182
previous_bookings_not_canceled    0.057365
days_in_waiting_list              0.054301
agent                             0.046770
adr                               0.046492
babies                            0.032569
stays_in_week_nights              0.025542
arrival_date_year                 0.016622
arrival_date_week_number          0.008315
arrival_date_day_of_month         0.005948
children                          0.004851
stays_in_weekend_nights           0.001323
Name: is_canceled, dtype: float64


df.groupby("is_canceled")["reservation_status"].value_counts()

is_canceled  reservation_status
0            Check-Out             75011
1            Canceled              42993
             No-Show                1206
Name: reservation_status, dtype: int64


exlusion_list = ["days_in_waiting_list", "arrival_date_year"]
# unnecessary features that are better removed; 
#either they have tiny impact on our model (low corelation)
# or have the same value in their column (all values are 2015 in the year column)


# we need to find out which columns are numerical values: (or not object)
# we need to define a loop to assess each column:
numerical_features = [col for col in df.columns if df[col].dtype!="O" and col not in exlusion_list]
numerical_features = numerical_features[:-1] # removing the date/time from numerical features


numerical_data = df[numerical_features]


# categorical features that need to be removed due to their tiny impact:
exclusion_list_cat = ["arrival_date_year", "assigned_room_type", "booking_changes", "reservation_status","country", "days_in_waiting_list"]


# using a loop to define the categorical features (columns):
categorical_features = [col for col in df.columns if df[col].dtype =='O' and col not in exclusion_list_cat]
categorical_features.append("reservation_status_date") #we put the date/time here as categorical feature
categorical_features

['hotel',
 'arrival_date_month',
 'meal',
 'market_segment',
 'distribution_channel',
 'reserved_room_type',
 'deposit_type',
 'customer_type',
 'reservation_status_date']


# creating a new dataset for categorical features:
data_cat = df[categorical_features]


# exctracting month/year/day from date column:
data_cat["res_year"] = data_cat["reservation_status_date"].dt.year
data_cat["res_month"] = data_cat["reservation_status_date"].dt.month
data_cat["res_day"] = data_cat["reservation_status_date"].dt.day
#dropping the "reservation_status_date" column:
data_cat.drop("reservation_status_date", axis = 1, inplace = True)


# adding "is_canceled" column to the categorical dataset:
data_cat ["cancellation"] = df["is_canceled"]


data_cat.head()


# we do the mean encoding on all the columns except "cancellation" [0:8]
inc_cols = data_cat.columns[0:8] 
# mean encoding on all columns with loop:
for col in inc_cols:
    dict = data_cat.groupby(col)["cancellation"].mean()
    data_cat[col] = data_cat[col].map(dict) #mapping the mean values to the columns


data_cat.head()


final_data = pd.concat([data_cat, numerical_data], axis = 1)
#removing the "is_canceled" drop due to the smililarity to the "cancellation" column;
final_data.drop("is_canceled",axis = 1, inplace = True) 
print(final_data.columns)
print(final_data.shape)

Index(['hotel', 'arrival_date_month', 'meal', 'market_segment',
       'distribution_channel', 'reserved_room_type', 'deposit_type',
       'customer_type', 'res_year', 'res_month', 'res_day', 'cancellation',
       'lead_time', 'arrival_date_week_number', 'arrival_date_day_of_month',
       'stays_in_weekend_nights', 'stays_in_week_nights', 'adults', 'children',
       'babies', 'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'booking_changes', 'agent', 'company',
       'adr', 'required_car_parking_spaces', 'total_of_special_requests'],
      dtype='object')
(119210, 29)


final_data.dropna(inplace=True)


# based on the nature of our data, it is easy to guess in which columns we might have outliers!
# we need to check "lead_time" and "adr"
sns.distplot(final_data["lead_time"])

<AxesSubplot:xlabel='lead_time', ylabel='Density'>


sns.distplot(final_data["adr"])

<AxesSubplot:xlabel='adr', ylabel='Density'>


# defining a funtion to handle the outliers:
def handle_outlier(data,col):
    data[col] = np.log1p(data[col])


handle_outlier(final_data,"adr")
handle_outlier(final_data,"lead_time")


# checking the results:
sns.distplot(final_data["adr"])

<AxesSubplot:xlabel='adr', ylabel='Density'>


sns.distplot(final_data["lead_time"])

<AxesSubplot:xlabel='lead_time', ylabel='Density'>


final_data.dropna(inplace = True)
y = final_data["cancellation"]
x = final_data.drop("cancellation", axis = 1)


from sklearn.linear_model import Lasso
from sklearn.feature_selection import SelectFromModel


feature_selection_model = SelectFromModel(Lasso(alpha = 0.005, random_state = 0))
# alpha is the penalty parameter (is a hyper parameter) which means the bigger the value, the less number of features will be selected;


#fitting the model:
feature_selection_model.fit(x,y)

SelectFromModel(estimator=Lasso(alpha=0.005, random_state=0))


feature_selection_model.get_support()
# False means the feature is not recommended for model and does not do much! and True is the other way around :D

array([False, False, False, False, False, False,  True, False,  True,
        True,  True,  True,  True, False, False, False,  True,  True,
       False, False,  True, False,  True, False,  True,  True,  True,
        True])


# let's find out which features are selected? we can do this by passing a filter to the "x" columns:
selected_features = x.columns[feature_selection_model.get_support()]
selected_features = selected_features.drop('res_year') #got higher scores when removed!!


x = x[selected_features]
x.head()


#preparing the datasets:
from sklearn.model_selection import train_test_split


from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score


x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.25, random_state = 1)


from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier


models =[]
models.append(("Logistic Regression", LogisticRegression()))
models.append(("Naive Bayes", GaussianNB()))
models.append(("RandomForest", RandomForestClassifier()))
models.append(("Decision Tree", DecisionTreeClassifier()))
models.append(("KNN", KNeighborsClassifier()))


 for name,model in models:
        print (">"*10 + "\t"+ name)
        
        model.fit(x_train, y_train)
        
        predictions = model.predict(x_test)
        print(f"\nConfusion Matrix:") 
        print(confusion_matrix(predictions,y_test))
        print("\n")
        print("Accuracy Score:\t")
        print(accuracy_score(predictions,y_test)) 
        print("\n"+"-"*60+"\n")

>>>>>>>>>>	Logistic Regression

Confusion Matrix:
[[17892  4997]
 [  900  6014]]


Accuracy Score:	
0.8021340133543603

------------------------------------------------------------

>>>>>>>>>>	Naive Bayes

Confusion Matrix:
[[ 8426  1146]
 [10366  9865]]


Accuracy Score:	
0.6137301613931483

------------------------------------------------------------

>>>>>>>>>>	RandomForest

Confusion Matrix:
[[18653  1206]
 [  139  9805]]


Accuracy Score:	
0.9548703150689528

------------------------------------------------------------

>>>>>>>>>>	Decision Tree

Confusion Matrix:
[[17960   799]
 [  832 10212]]


Accuracy Score:	
0.9452739657081501

------------------------------------------------------------

>>>>>>>>>>	KNN

Confusion Matrix:
[[18571  1398]
 [  221  9613]]


Accuracy Score:	
0.9456766097372747

------------------------------------------------------------

	country	no. of guests
0	PRT	20977
1	GBR	9668
2	FRA	8468
3	ESP	6383
4	DEU	6067
...	...	...
161	SMR	1
162	GUY	1
163	NCL	1
164	NPL	1
165	MLI	1

	arrival_date_month	adr
0	April	75.867816
1	August	181.205892
2	December	68.410104
3	February	54.147478
4	January	48.761125
5	July	150.122528
6	June	107.974850
7	March	57.056838
8	May	76.657558
9	November	48.706289
10	October	61.775449
11	September	96.416860

	arrival_date_month	adr
0	April	111.962267
1	August	118.674598
2	December	88.401855
3	February	86.520062
4	January	82.330983
5	July	115.818019
6	June	117.874360
7	March	90.658533
8	May	120.669827
9	November	86.946592
10	October	102.004672
11	September	112.776582

	month	price_for_resort	price_for_city_hotel
0	April	75.867816	111.962267
1	August	181.205892	118.674598
2	December	68.410104	88.401855
3	February	54.147478	86.520062
4	January	48.761125	82.330983
5	July	150.122528	115.818019
6	June	107.974850	117.874360
7	March	57.056838	90.658533
8	May	76.657558	120.669827
9	November	48.706289	86.946592
10	October	61.775449	102.004672
11	September	96.416860	112.776582

	month	price_for_resort	price_for_city_hotel
0	January	48.761125	82.330983
1	February	54.147478	86.520062
2	March	57.056838	90.658533
3	April	75.867816	111.962267
4	May	76.657558	120.669827
5	June	107.974850	117.874360
6	July	150.122528	115.818019
7	August	181.205892	118.674598
8	September	96.416860	112.776582
9	October	61.775449	102.004672
10	November	48.706289	86.946592
11	December	68.410104	88.401855

Main Goal:¶

Problem Statements:¶

Examining the data:¶

we need to check three columns: Adult, Children, and Babies to see if there is a row with all these three values as zeros at the same time! It is not possible to have all three as zeros.¶

NEXT STEP:¶

>> Finding Corelations¶

Categorical Features Dataset:¶

Mean Encoding:¶

Concatenating numerical and categorical databases:¶

Handling the outliers:¶

Defining the features:¶

Feature importance:¶

Applying machine learning algorithm:¶

Deploying Models:¶

	hotel	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_week_nights	adults	...	deposit_type	agent	company	customer_type	adr	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	342	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	7/1/2015
1	Resort Hotel	737	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	7/1/2015
2	Resort Hotel	7	2015	July	27	1	1	1	...	No Deposit	NaN	NaN	Transient	75.0	0	Check-Out	7/2/2015
3	Resort Hotel	13	2015	July	27	1	1	1	...	No Deposit	304.0	NaN	Transient	75.0	0	Check-Out	7/2/2015
4	Resort Hotel	14	2015	July	27	1	2	2	...	No Deposit	240.0	NaN	Transient	98.0	1	Check-Out	7/3/2015

	hotel	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_week_nights	adults	...	deposit_type	agent	customer_type	adr	reservation_status	reservation_status_date
40060	City Hotel	6	2015	July	27	1	2	1	...	No Deposit	6.0	Transient	0.00	Check-Out	2015-07-03
40066	City Hotel	3	2015	July	27	2	3	1	...	No Deposit	1.0	Transient-Party	58.67	Check-Out	2015-07-05
40070	City Hotel	43	2015	July	27	3	2	2	...	No Deposit	1.0	Transient-Party	86.00	Check-Out	2015-07-05
40071	City Hotel	43	2015	July	27	3	2	2	...	No Deposit	1.0	Transient-Party	43.00	Check-Out	2015-07-05
40072	City Hotel	43	2015	July	27	3	2	2	...	No Deposit	1.0	Transient-Party	86.00	Check-Out	2015-07-05

	month	no. of guests
0	August	3257
1	July	3137
2	October	2575
3	March	2571
4	April	2550
5	May	2535
6	February	2308
7	September	2102
8	June	2037
9	December	2014
10	November	1975
11	January	1866

	month	no. of guests
0	August	5367
1	July	4770
2	May	4568
3	June	4358
4	October	4326
5	September	4283
6	March	4049
7	April	4010
8	February	3051
9	November	2676
10	December	2377
11	January	2249

	is_canceled	lead_time	arrival_date_year	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	children	babies	is_repeated_guest	previous_cancellations	previous_bookings_not_canceled	booking_changes	agent	company	days_in_waiting_list	adr	required_car_parking_spaces	total_of_special_requests
is_canceled	1.000000	0.292876	0.016622	0.008315	-0.005948	-0.001323	0.025542	0.058182	0.004851	-0.032569	-0.083745	0.110139	-0.057365	-0.144832	-0.046770	-0.083594	0.054301	0.046492	-0.195701	-0.234877
lead_time	0.292876	1.000000	0.040334	0.127046	0.002306	0.085985	0.166892	0.117575	-0.037878	-0.021003	-0.123209	0.086025	-0.073599	0.002230	-0.013114	-0.085854	0.170008	-0.065018	-0.116624	-0.095949
arrival_date_year	0.016622	0.040334	1.000000	-0.540373	-0.000121	0.021694	0.031203	0.030266	0.054710	-0.013192	0.010281	-0.119905	0.029234	0.031416	0.056438	0.033682	-0.056348	0.198429	-0.013812	0.108610
arrival_date_week_number	0.008315	0.127046	-0.540373	1.000000	0.066572	0.018629	0.016047	0.026567	0.005556	0.010417	-0.031125	0.035493	-0.021009	0.006311	-0.018225	-0.032912	0.022677	0.076281	0.001980	0.026202
arrival_date_day_of_month	-0.005948	0.002306	-0.000121	0.066572	1.000000	-0.016225	-0.028362	-0.001754	0.014550	-0.000235	-0.006471	-0.027027	-0.000306	0.011266	0.000159	0.003667	0.022532	0.030291	0.008569	0.003026
stays_in_weekend_nights	-0.001323	0.085985	0.021694	0.018629	-0.016225	1.000000	0.494175	0.094759	0.046135	0.018607	-0.086009	-0.012769	-0.042859	0.050191	0.162411	-0.080783	-0.054399	0.050670	-0.018520	0.073124
stays_in_week_nights	0.025542	0.166892	0.031203	0.016047	-0.028362	0.494175	1.000000	0.096214	0.044652	0.020373	-0.095302	-0.013976	-0.048873	0.080018	0.196777	-0.044437	-0.002026	0.066847	-0.024933	0.068738
adults	0.058182	0.117575	0.030266	0.026567	-0.001754	0.094759	0.096214	1.000000	0.029409	0.017890	-0.140973	-0.007070	-0.108856	-0.041472	0.023370	-0.166182	-0.008365	0.224253	0.014438	0.123353
children	0.004851	-0.037878	0.054710	0.005556	0.014550	0.046135	0.044652	0.029409	1.000000	0.023999	-0.032475	-0.024755	-0.021078	0.051000	0.050461	-0.042554	-0.033293	0.325057	0.056247	0.081747
babies	-0.032569	-0.021003	-0.013192	0.010417	-0.000235	0.018607	0.020373	0.017890	0.023999	1.000000	-0.008813	-0.007509	-0.006552	0.085605	0.030235	-0.009426	-0.010627	0.029043	0.037389	0.097939
is_repeated_guest	-0.083745	-0.123209	0.010281	-0.031125	-0.006471	-0.086009	-0.095302	-0.140973	-0.032475	-0.008813	1.000000	0.082740	0.420642	0.013044	-0.051584	0.161871	-0.022057	-0.130807	0.077928	0.012963
previous_cancellations	0.110139	0.086025	-0.119905	0.035493	-0.027027	-0.012769	-0.013976	-0.007070	-0.024755	-0.007509	0.082740	1.000000	0.152570	-0.027261	-0.018251	-0.001110	0.005941	-0.065974	-0.018540	-0.048488
previous_bookings_not_canceled	-0.057365	-0.073599	0.029234	-0.021009	-0.000306	-0.042859	-0.048873	-0.108856	-0.021078	-0.006552	0.420642	0.152570	1.000000	0.011963	-0.046348	0.111220	-0.009416	-0.072335	0.047506	0.037775
booking_changes	-0.144832	0.002230	0.031416	0.006311	0.011266	0.050191	0.080018	-0.041472	0.051000	0.085605	0.013044	-0.027261	0.011963	1.000000	0.038555	0.089768	-0.011916	0.026601	0.067490	0.055003
agent	-0.046770	-0.013114	0.056438	-0.018225	0.000159	0.162411	0.196777	0.023370	0.050461	0.030235	-0.051584	-0.018251	-0.046348	0.038555	1.000000	-0.121333	-0.041182	0.015711	0.119282	0.060783
company	-0.083594	-0.085854	0.033682	-0.032912	0.003667	-0.080783	-0.044437	-0.166182	-0.042554	-0.009426	0.161871	-0.001110	0.111220	0.089768	-0.121333	1.000000	-0.022944	-0.127641	0.038638	-0.090790
days_in_waiting_list	0.054301	0.170008	-0.056348	0.022677	0.022532	-0.054399	-0.002026	-0.008365	-0.033293	-0.010627	-0.022057	0.005941	-0.009416	-0.011916	-0.041182	-0.022944	1.000000	-0.040859	-0.030601	-0.082755
adr	0.046492	-0.065018	0.198429	0.076281	0.030291	0.050670	0.066847	0.224253	0.325057	0.029043	-0.130807	-0.065974	-0.072335	0.026601	0.015711	-0.127641	-0.040859	1.000000	0.056510	0.172308
required_car_parking_spaces	-0.195701	-0.116624	-0.013812	0.001980	0.008569	-0.018520	-0.024933	0.014438	0.056247	0.037389	0.077928	-0.018540	0.047506	0.067490	0.119282	0.038638	-0.030601	0.056510	1.000000	0.082718
total_of_special_requests	-0.234877	-0.095949	0.108610	0.026202	0.003026	0.073124	0.068738	0.123353	0.081747	0.097939	0.012963	-0.048488	0.037775	0.055003	0.060783	-0.090790	-0.082755	0.172308	0.082718	1.000000

	hotel	arrival_date_month	meal	market_segment	distribution_channel	reserved_room_type	deposit_type	customer_type	res_year	res_month	res_day
0	Resort Hotel	July	BB	Direct	Direct	C	No Deposit	Transient	2015	7	1
1	Resort Hotel	July	BB	Direct	Direct	C	No Deposit	Transient	2015	7	1
2	Resort Hotel	July	BB	Direct	Direct	A	No Deposit	Transient	2015	7	2
3	Resort Hotel	July	BB	Corporate	Corporate	A	No Deposit	Transient	2015	7	2
4	Resort Hotel	July	BB	Online TA	TA/TO	A	No Deposit	Transient	2015	7	3

	hotel	arrival_date_month	meal	market_segment	distribution_channel	reserved_room_type	deposit_type	customer_type	res_year	res_month	res_day
0	0.277674	0.374644	0.374106	0.153712	0.174868	0.330827	0.28402	0.407864	2015	7	1
1	0.277674	0.374644	0.374106	0.153712	0.174868	0.330827	0.28402	0.407864	2015	7	1
2	0.277674	0.374644	0.374106	0.153712	0.174868	0.391567	0.28402	0.407864	2015	7	2
3	0.277674	0.374644	0.374106	0.187618	0.220568	0.391567	0.28402	0.407864	2015	7	2
4	0.277674	0.374644	0.374106	0.367590	0.410598	0.391567	0.28402	0.407864	2015	7	3

	deposit_type	res_month	res_day	lead_time	arrival_date_week_number	adults	booking_changes	adr	total_of_special_requests
0	0.28402	7	1	5.837730	27	2	3	0.000000	0
1	0.28402	7	1	6.603944	27	2	4	0.000000	0
2	0.28402	7	2	2.079442	27	1	0	4.330733	0
3	0.28402	7	2	2.639057	27	1	0	4.330733	0
4	0.28402	7	3	2.708050	27	2	0	4.595120	1